Prediction of Congestion and Bursting Phenomena in Network Traffic Based on Multifractal Spectrums

Abstract

From the viewpoint of nonlinear dynamics, a numerical method for predicting the traffic of the local area network (LAN) is presented based on the multifractal spectrums; in particular, for predicting the typical congestion and bursting phenomena, by analyzing real time sequences. First, the multifractal spectrums available to the LAN traffic are derived in some detail and their physical meanings are consequently explained. Then an exponent factor is introduced to the measurement or description of the singularity of the time sequence and the correlations between multifractal spectrums and traffic flow rate are studied in depth. Finally, as an example, the multifractal spectrums presented are used to predict the network traffic of an Ethernet by analyzing its real time sequence. The results show that there exists a distinct relationship between the multifractal spectrums and the traffic flow rate of networks and the multifractal spectrum could be used to efficiently and feasibly predict the traffic flow rate, especially for predicting the singularities of the real time sequences, which are closely related to the congestion and bursting phenomena. Thus, this method can be applied to the prediction and management of the congestion and bursting in the network traffic at an early time. Furthermore, the prediction will become much more accurate and powerful over a long period, since the fluctuations of the traffic flow rate are remarkable.